1. 1 Key Uses of Household Survey Data Kathleen Beegle Workshop 17, Session 1b Designing and Implementing Household Surveys March 31, 2009

2. 2 Wide range of uses Respond to the demand for data for performance-based management. But it is also used for other purposes. Broad categories of use: 1. Basic Diagnostics of Living Standards: MDGs, PRSPs, Poverty assessments, Poverty Maps 2. Evaluation/development of programs: PSIA, Proxy Means 3. Studies of development processes

3. 3 Millennium Development Goals (MDGs) Many, but not all, MDG indicators are captured in an LSMS (IS). Some can be measures with adaptation (if there is lack of other data sources for that indicator, such as IMR/immunization histories). Other indicators require either larger samples than a typical LSMS (MMR), or administrative/other data.

4. 4 MDGs 1 -3

5. 5 MDGs 4 -8

6. Secondary school enrollments, 12-18 year olds, Albania 2002 Average Percent In almost all countries we have a single statistic: mean enrollment at the national level. In this case it is 61%. This is interesting for monitoring purposes, but it doesn’t say much about poverty or other factors.

7. Average Percent In many countries we have regional breakdowns, with marked contrasts The contrast between urban and rural rates emphasizes the disadvantages faced by rural communities. Other breakdowns would be useful Urban Rural Secondary school enrollments, 12-18 year olds, Albania 2002

8. Percent …With the LSMS survey we can show enrollment rates broken down by consumption level-- and thus understand an additional dimension Consumption quintile Female, urban Male, urban Male, rural Female, rural Average Secondary school enrollments, 12-18 year olds, Albania 2002

9. 9 Poverty Maps Not necessarily “maps”, rather highly disaggregated databases of poverty and inequality. This disaggregation is usually spatial. Demand for poverty maps: geographic targeting of anti-poverty programs, decentralization and evidence-based policy,… Linking LSMS/IS data to Census data to impute welfare levels in small areas www.worldbank.org/povertymapping

10. 10 Example: Yunnan Province (China)

11. 11 Poverty and Social Impact Analysis: PSIA Analysis of consequences and distributional impacts of policy interventions/reforms, such as:  Utilities  Pension reforms  Civil service reform  Ag reform  Education/health (fees, decentralization)  Fiscal (VAT, other taxes)  Land reforms  Etc… http: //www.worldbank.org/psia

12. 12 Tools for PSIA Volume of case studies (Coudouel, Dani and Paternostro 2006)

13. 13 Example: Malawi ADMARC reforms Restructuring marketing functions of ADMARC (closing loss-making markets for inputs and outputs) Objective: Investigate the importance of ADMARC services for various groups Data: 1997/98 Malawi Integrated Household Survey, merged with location of ADMARC markets and roads network

14. 14 Malawi ADMARC reforms Proximity has a larger positive effect in remote areas:  Impact of markets on maize yields, demand for fertilizer farm profits and consumption is significant only in remote areas. Policy recommendations:  In areas where the private sector operates and which are close to a main road, loss-making markets could be closed without major distributional impacts.  In areas where the private sector does not operate and where households are isolated, subsidy to loss-making markets could be justified for their social role.

15. 15 Such analysis can also be used for ex-post evaluation/assessment of policy/events Evaluation of impact of Malawi fertilizer voucher scheme by re-surveying subset of IHS 2004/05 households in 2006/07. Rapid assessment of impact of Hurricane Mitch: Shortly after completing the 1998 LSMS Nicaragua, returned to households in sample in the areas affected. Nicaragua Social Fund (FISE) evaluation by over-sampling FISE areas (booster sample in 1 st stage) and linking with 1998 LSMS

16. 16 Proxy Means Testing for Programs Who should be beneficiaries? How to identify these people? (Other uses of household survey data that influence program design: Geographic coverage; level benefits people receive) Using household survey data to develop short list of simple indicator that can be collected in the field to “proxy” the household income/consumption. Compile long list of possible indicators, then use econometrics to determine which indicators are useful and the weight to place on these indicators. Analysis can be made more accurate by using more specific geographic regions (urban/rural, districts, etc) but this depends on the level at which results can be generalized from household data.

17. 17 Proxy Means Testing: Examples KIHBS 2007 data being used to create targeting system for OVC CCT program that targets poorest 20%. Panama Red de Oportunidades CCT program, developed with input from the 2003 Panama Living Standards Survey (Encuesta de Niveles de Vida, ENV)

18. 18 Understanding development & living standards Example from Vietnam: What are the long- lasting effects of conflict events, in levels (poverty), and in changes (growth)?  Detailed information on health and disability from VHLSS 2006  Panel structure to look at changes  Data on US military activities (bombing and herbicide spray applications)

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20. 20 Summary Household surveys like an LSMS can help monitor welfare, as well as influence the design and implementation of social policy. They are also a tool for studying development and living standards more generally. The extent of these applications will depend on, among other factors:  Comparability with existing data  Developing questionnaire/sample to respond to needs  Coordination with others (country teams, other groups)  Public availability of well-documented data

21. 21 Summary Using existing data as a source for conducting an evaluation: Need to understand the sample design and content of the questionnaire. There is potential to embed an evaluation into a household survey, through piggy-backing (adding questions or a booster sample) or creating a panel (fielding a subsequent round). To be discussed later.